QDK · Adaptive Optimiser · preview

AI & Quantum Linux Kernel Adaptive Optimiser

One model that unifies the AI surface (insights, RAG, agentic copilots, SLM, neural feedback) with the Quantum Dev Kit stack — SDK, runtime, classical-quantum bridge and QHAL — across Linux, Windows, QNX, VxWorks and macOS.

QDK stack · top → bottom

APPQuantum ApplicationsDeveloper apps — chemistry, optimisation, ML, cryptography.

SDKQDK SDK · Unified Quantum APISingle surface across Qiskit, Cirq, Braket, PennyLane, local simulators.

RTQDK Runtime + Classical-Quantum BridgeJob graphs, hybrid kernels, shot batching, mid-circuit measurement, parameter binding.

HALQHAL · Quantum Hardware AbstractionBackend-agnostic pulses, gates, qubit topology, error models, calibration cache.

OSOS LayerLinux · Windows · QNX · VxWorks · macOS · Yocto (PREEMPT-RT).

HWHardwareQPU · CPU · GPU · NPU · TPU · cryo control electronics.

AI surface inside the optimiser

AI Insights

Telemetry → root-cause + recommendations on scheduler decisions.

Inferencing nudge

Live hints in the editor: 'switch to int8', 'co-locate shard 3 on leaf-02'.

AI Assist

Chat copilot grounded in cluster state and QDK docs.

Data Science

Notebooks wired to telemetry Parquet/DuckDB, sklearn + JAX preinstalled.

RAG

Hybrid search over runbooks, kernel symbols, QHAL calibration logs.

Agentic AI

Goal-driven agents: 'reduce p99 < 12ms', tool-use over kubectl/helm/qdkctl.

SLM

On-device small language model for offline edge nudges (≤3B params, int4).

Data Pipelines

Streaming Arrow/Flight → Vector DB + Parquet lake.

Vector DB

pgvector + LanceDB for embeddings of kernel events and code.

Neural Network OS

Scheduler is itself a small policy net trained on feedback.

Neural Feedback Loop

Online RL signal: latency, power, fabric headroom → policy update.

Knowledge Graphs

Symbols × hardware × jobs × incidents, queried by the agent.

Network Graphs

Live leaf/spine topology with congestion overlays.

Kernel integration approaches

Approach A2 · Userspace Runtime

Like CUDA / ROCm / OpenCL talking to a thin kernel driver.

QDK Runtime lives entirely in userspace
Talks to existing kernel drivers via /dev/qpu0
Fast iteration, language-level SDKs
Containerisable, K8s device-plugin friendly

Pros

Fast to ship, portable, easy to debug.

Cons

Extra syscall hops, weaker isolation guarantees vs in-kernel.

app  →  libqdk.so  →  /dev/qpu0  →  thin kernel driver  →  control HW  →  QPU

Domain × Yocto × OS guidance

Domain	Yocto?	OS / Platform options	Notes
AI/HPC Infrastructure	No	Ubuntu, RHEL, Debian, Custom Linux	Ecosystem, drivers, fast iteration matter more than build-system control
Embedded / Industrial IoT	Yes	Linux (Yocto), VxWorks, QNX, Zephyr	Reproducible BSP, minimal footprint, hardware-specific
Automotive	Yes	Linux (Yocto + AGL), QNX, VxWorks, AUTOSAR	Safety standards, determinism, BSP control
Space / Aerospace	Yes	Linux (Yocto hardened), VxWorks, RTEMS	Radiation tolerance, determinism, certification
Industrial Robotics	Yes	Linux (Yocto), QNX, VxWorks, ROS2	Real-time, deterministic, fieldbus support
QDK Control Electronics	Yes	Linux (Yocto), VxWorks, QNX	Low-latency quantum control hardware
QDK SDK / Dev Tools	No	Ubuntu, macOS, Windows	Developer tooling, Python ecosystem

OS / Platform · real-time and licensing matrix

OS / Platform	Type	Real-time	Open source	Typical domain
Linux (Yocto)	GPOS + RT possible	With PREEMPT-RT	Yes	Embedded, IoT, Automotive, Space
Linux (Ubuntu)	GPOS	With PREEMPT-RT	Yes	AI/HPC, Cloud, Server, Dev
VxWorks	RTOS	Yes, hard RT	No, proprietary	Aerospace, Defense, Industrial, Space
QNX	RTOS / POSIX	Yes, hard RT	No, proprietary	Automotive, Medical, Industrial
Zephyr	RTOS	Yes, hard RT	Yes	Ultra-low-power IoT, MCU-class
RTEMS	RTOS	Yes, hard RT	Yes	Space, NASA, ESA certified
FreeRTOS	RTOS	Yes, hard RT	Yes	MCU-class embedded
AUTOSAR	Middleware/OS	Yes	No	Automotive ECU